AI · Sub-service
RAG & Knowledge Systems
Grounded retrieval over docs, tickets, wikis, and product data — with citations, ACL awareness, and eval loops.
Summary
Grounded retrieval over docs, tickets, wikis, and product data — with citations, ACL awareness, and eval loops.
Problem
What teams are stuck with
Teams dump PDFs into a vector store and wonder why answers hallucinate or leak across tenants.
Without chunking strategy, metadata filters, and hybrid search, retrieval confidence collapses under real corpora.
Offerings
What we deliver
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01
Corpus design and chunking strategy
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02
Hybrid search + re-rank pipelines
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03
Permission-aware retrieval
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04
Citation and source UX
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05
Continuous evaluation harnesses
Approach
How we work it
Map sources, access control, and refresh cadence before picking an embedding model.
Hybrid retrieval (keyword + dense), re-ranking, and citation UX are default — not optional upgrades.
We instrument hit-rate, faithfulness, and latency so RAG quality is a product metric.
Outcomes
What good looks like
- Assistants that answer with sources operators trust
- Lower support load on known knowledge
- A retrieval layer reusable by multiple products
Related
Keep exploring
Build this with Endeavour
Share the workflow, corpus, or product surface — we’ll respond with a concrete next step.